2021
Clinician Trends in Prescribing Direct Oral Anticoagulants for US Medicare Beneficiaries
Wheelock KM, Ross JS, Murugiah K, Lin Z, Krumholz HM, Khera R. Clinician Trends in Prescribing Direct Oral Anticoagulants for US Medicare Beneficiaries. JAMA Network Open 2021, 4: e2137288. PMID: 34870678, PMCID: PMC8649845, DOI: 10.1001/jamanetworkopen.2021.37288.Peer-Reviewed Original ResearchConceptsDOAC useAnticoagulant prescriptionOral anticoagulantsUS cliniciansMedicare beneficiariesNational clinical practice guidelinesElevated bleeding riskOral anticoagulant prescriptionsRetrospective cohort studyDirect oral anticoagulantsClinical practice guidelinesUS Medicare beneficiariesInternal medicine physiciansNumber of cliniciansAnticoagulant prescribingDOAC prescriptionsUnique cliniciansBleeding riskCohort studyAnticoagulant strategiesPrescription claimsPractice guidelinesMAIN OUTCOMEMost indicationsMedicare population
2019
Digoxin Use and Associated Adverse Events Among Older Adults
Angraal S, Nuti SV, Masoudi FA, Freeman JV, Murugiah K, Shah ND, Desai NR, Ranasinghe I, Wang Y, Krumholz HM. Digoxin Use and Associated Adverse Events Among Older Adults. The American Journal Of Medicine 2019, 132: 1191-1198. PMID: 31077654, DOI: 10.1016/j.amjmed.2019.04.022.Peer-Reviewed Original ResearchConceptsRate of hospitalizationDigoxin useDigoxin toxicityNational Prescription AuditMedicare feeService beneficiariesDigoxin prescriptionAssociated adverse eventsAdverse eventsCohort studyAdverse outcomesSecondary diagnosisNational cohortPrescription auditPrescription trendsClinical guidelinesHospitalizationMortality rateClinical practiceOlder adultsSubsequent outcomesOutcomesToxicityPrescriptionNational-level trends
2018
Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
Huang C, Murugiah K, Mahajan S, Li SX, Dhruva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLOS Medicine 2018, 15: e1002703. PMID: 30481186, PMCID: PMC6258473, DOI: 10.1371/journal.pmed.1002703.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedClinical Decision-MakingData MiningDecision Support TechniquesFemaleHumansMachine LearningMaleMiddle AgedPercutaneous Coronary InterventionProtective FactorsRegistriesReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeConceptsPercutaneous coronary interventionNational Cardiovascular Data RegistryRisk prediction modelAKI eventsAKI riskCoronary interventionAKI modelMean ageCardiology-National Cardiovascular Data RegistryAcute kidney injury riskAKI risk predictionRetrospective cohort studyIdentification of patientsCandidate variablesAvailable candidate variablesCohort studyPCI proceduresPoint of careBrier scoreAmerican CollegeData registryPatientsCalibration slopeInjury riskSame cohort